Welcome to svdynamics! Support Vector Dynamics is a lightweight, scikit-learn
compatible Python library for building and using mixed (composite) kernels for
support vector machines. It provides a simple and extensible interface for
combining multiple kernel functions into a single weighted kernel, while
remaining fully compatible with existing sklearn pipelines, cross-validation,
and calibration workflows.
svdynamics focuses on making kernel composition a first-class modeling primitive
for both classification and regression, without requiring any changes to the
underlying scikit-learn API.
Highlights#
Additive (weighted) composite kernels
Drop-in replacement for sklearn SVC / SVR
Compatible with pipelines, GridSearchCV, calibration and resampling
Designed to integrate cleanly with existing ML workflows